These short range gate radars, are moreover non-ambiguous in distance. They may be frequency-agile in the X-band, around 10 GHz.
At the operating frequencies of airborne radars, rainfall generates echoes that are often non-negligible and possibly of comparable power to ground echoes in certain cases where heavy rainfalls are present. The presence of rain echoes may then cause a forced shutdown of the operating modes addressed, or indeed carrier navigation errors.
Discriminating surface echoes from atmospheric echoes, in particular those due to rainfall, is a problem often encountered in meteorological applications where surface radars are employed. Most of them operate in S or C band, where the attenuation of the waves during their propagation in the rainfall is not inconvenient, even though some of them are designed to operate in X-band because, in particular, of the compactness and cost advantages, as described in particular in the document by C. Z. van de Beek et al., “Performance of High-Resolution X-Band Radar for Rainfall Measurement in the Netherlands”, Hydrol. Earth Syst. Sci. Discuss., vol. 6, pp. 6035-6085, September 2009. There also exists a Ku-band spatial radar dedicated to measurements of tropical precipitations. In contradistinction to the problem addressed by the invention, it is the ground echoes which are then considered to be an inconvenience whilst the rain echoes are of principal interest—besides, the measurements provided by meteorological radars are provided directly as a specific unit of “reflectivity” (in mm6/m3, denoted Z) which is relevant for estimating an amount of rainfall in a given zone.
Although Doppler or polarimetry information turns out to be useful, in numerous cases only the measurements of power (or of reflectivity) in the domain instrumented in terms of distances and angles are available for specifying the provenance of the received echoes. The most restricted framework in which the invention lies is that of incoherent and non-polarimetric radar, although it remains more generally valid.
In the cases of surface meteorological radars, the ground echoes conventionally due to the relief and objects encountered can be instrumented beforehand in dry weather, and then used to eliminate ground echoes and/or to correct the measurements of rainfall in accordance with procedures based on interpolation, nearest neighbors, weighting, or elementary tracking of rainfall zones. The choice of the installation site is also definitely significant. This “static” approach is obviously not simply applicable to the case of a mobile radar. A possible alternative is to use information sources external to the radar to locate the rainfall zones, such as digital terrain models, a network of meteorological radars or other information sources but solutions of this type depart from the framework of the invention.
However, the most inconvenient echoes in meteorology are those due to propagation anomalies, themselves due to certain schemes of variations of the refractive index of the medium over the large distances probed (hundreds of kilometers). The most problematic conditions are those of “super-refraction”, where a fraction of the radar beam (main or secondary lobe) is deviated toward the ground and may moreover be trapped in a propagation conduit close to the ground as in a waveguide. Under such conditions, it may happen that the radar beam touches the ground several times over hundreds of kilometers of distance, creating numerous and powerful undesirable echoes comparable to echoes of heavy rainfalls. In order to isolate these latter, dynamic approaches are necessary since the propagation conditions are by nature variable in time and space, and therefore difficult to foresee and characterize. Although the context is different from the short-range radar modes forming the subject of the invention, it is instructive to examine the way in which the non-foreseeable ground echoes are handled in publications.
The very great majority of published approaches deal with a radar map comprising the measurements in an angle/distance plane. They define various criteria on the basis of the measurement map, in a more or less ad hoc way, which thereafter feed decision trees (see the document by U. Germann et al., “Radar Precipitation Measurement in a Mountainous Region”, Q.J.R. Meteorol. Soc., vol. 132, pp. 1669-1692, 2006), Bayesian classifiers (see the document by J. C. Nicol et al., “Techniques for Improving Ground Clutter Identification”, Proc. Weather Radar and Hydrology, Exeter (UK), April 2011), or neural networks (see the document by V. Lakshmanan et al., “An Automated Technique to Quality Control Radar Reflectivity Data”, J. Applied Meteorology, vol. 46, no. 3, pp. 288-305, March 2007), sometimes with the use of fuzzy logic such as for example in the document by Y. Li et al., “A New Approach to Detect the Ground Clutter Mixed with Weather Echoes”, IEEE Radar Conference, pp. 622-626, Kansas City (Mo.), USA, May 23-27, 2011. Among the most used criteria may be cited the vertical extent of horizontal or vertical reflectivity gradient (or indeed the 3D structure of the reflectivity field), which has shown itself to be effective for discriminating surface echoes, including sea echoes, under a condition of super-refraction. Reflectivity profiles are moreover also used in the document by B. Geerts, “Airborne Radar and Passive Microwave-Based Classification and Characterization of Tropical Precipitation Profiles”, 30th International Conference on Radar Meteorology, Munich, Germany, Jul. 18-24, 2001, in an airborne and space context for distinguishing precipitations of convective type from those of stratiform type. The reflectivity gradient is however not transposable to the airborne case since it is a criterion which reflects the physics of propagation with upward sighting from the ground, which is very particular to meteorological radars as shown in particular in the document by P. P. Alberoni et al., “Use of the Vertical Reflectivity Profile for Identification of Anomalous Propagation”, Meteorol. Appl., vol. 8, pp. 257-266, 2001.
Other criteria that may possibly be employed with incoherent radars are those related to temporal fluctuations from pulse to pulse as in the document by J. Sugier et al., “Detection and Removal of Clutter and Anaprop in Radar Data Using a Statistical Scheme Based on Echo Fluctuation”, Proc. ERAD'02, pp. 17-24, 2002, or from pointing to pointing of one and the same zone as in the document by Y. Li et al., “Scan-to-Scan Correlation of Weather Radar Signals to Identify Ground Clutter”, IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 855-859, July 2013. These approaches are based on the relative stationarity of the power of the surface echoes with respect to the rain echoes. They are flawed in the context of the invention because on the one hand of the fast displacement of the carrier during the emission of the radar pulses, and on the other hand because of the frequency agility from pulse to pulse, which is characteristic of certain modes, and rendering the ground echoes fluctuating.
Finally, more general texture criteria, arising from the field of image processing and based on co-occurrence matrices as in particular in the document by O. Raaf, A. Adane, “Image-Filtering Techniques for Meteorological Radar”, IEEE International Symposium on Industrial Electronics (ISIE), pp. 2561-2565, Cambridge, Jun. 30-Jul. 2, 2008—that may be calculated fast—have also been proposed. However, the choice of the “offsets” for the calculation of the co-occurrence matrix has a significant impact on the results and there is no clearly established procedure for selecting them.
In another vein, theoretical and empirical studies have revealed a self-similar character in precipitation fields, making it possible to appraise their very great variability, both temporal and spatial, as for example described in the document by T. M. Over, V. J. Gupta, “A Space-Time Theory of Mesoscale Rainfall using Random Cascades”, J. Geophys. Res., vol. 101, pp. 26319-26331, 1996. In the document by B. Haddad et al., “Analyse de la Dimension Fractale des échos radar en Algérie, France et Sénégal” [Analysis of the Fractal Dimension of radar echoes in Algeria, France and Senegal], Télédétection, vol. 5, no 4, pp. 299-306, 2006, the fractal dimension of the rain echoes in the radar images does not suffice to properly distinguish them from ground echoes, thereby justifying the switch to a finer multi-fractal analysis. Multi-fractal analysis is exploited with more success in particular in the document by M. Khider et al., “Analyse Multifractale des Echos Radar par la Méthode des Maximums des Modules de la Transformée en Ondelette (MMTO) 2D pour les Sites de Bordeaux (France), Setif (Algérie): Application à l'Elimination des Echos Parasites” [Multi-fractal Analysis of Radar Echoes by the Method of Maxima of the 2D Wavelet Transform Moduli for the Sites of Bordeaux (France), Sétif (Algeria): Application to the Elimination of the Spurious Echoes], Revue Télédétection, vol. 8, no. 4, pp. 271-283, 2008, to separate surface echoes from rain echoes, or else in various works for simulating and forecasting precipitation fields as described for example in the document by D. E. Rupp et al., Multiplicative Cascade Models for Fine Spatial Downscaling of Rainfall: Parametrization with Rain Gauge Data, Hydrol. Earth. Syst. Sci., vol. 16, pp. 671-684, 2012. This type of approach seems interesting but requires a relatively high complexity of implementation. Moreover, the choice of the relevant multi-fractal coefficients is not obvious a priori.
Employed jointly, the criteria mentioned are more or less characteristic of ground echoes (in particular those arising from abnormal propagation) but generally implicitly assume that the radar does not move and sights upward, which hypothesis is actually satisfied in the case of surface radars but not in the case of airborne radars possessing a non-zero speed of their own.
Another general defect of the numerous approaches making use of the diverse criteria hereinabove is that they involve in one way or another a phase of learning with suites of calibration data. However, in the context of the invention, such a phase is excluded since the environment is changing rapidly and it is not always possible to have the data required or the time to properly conduct a calibration phase.